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There are steps to consider when it comes to considering how to use data to make decisions in your content strategy.
As a professional in the field of content performance, I have witnessed firsthand how data can transform decision-making processes. It takes the guessing and opinion-based debates out of the discussion.
Data-driven decision-making involves using available data to guide decision-making processes and determining what steps should be taken next.
Key Points:
- Data-driven decision-making involves using available data to guide decision-making.
- The process of using data involves collecting relevant data and organizing and analyzing it effectively.
- Challenges such as data quality, biases, and data interpretation can be overcome with the right mindset.
On the podcast episode with Kevin Hanegan, we discuss:
- Why is data literacy so important?
- Why is data literacy so important?
- The key to being data literate is politely questioning and challenging the data, said Kevin.
- How to make the team more collaborative.
- The problem of being right or wrong in marketing.
- What is the hierarchy of analytics?
Collecting Relevant Data to Drive Content Performance
You can’t answer the question of if your content is driving results if you aren’t collecting the right data.
First, identify the sources of data that are most relevant to your business storytelling strategy. Some common metrics include:
- Organic traffic – including search impressions, clicks, and keywords ranked for
- Engagement metrics like time on page, scroll depth, etc.
- Brand awareness
- Conversions to leads and sales opportunities
Identifying Reliable Data Sources
When collecting data, it’s important to ensure that the sources are reliable. Look for sources that are well-established and have a track record of providing accurate data. When it comes to content strategy and performance, consider using:
- Google Search Console
- Google Analytics
- HubSpot
- Salesforce
Leveraging Technology for Data Collection
Technology can help streamline the process, reduce errors, and provide real-time information. Consider using a dashboard to look at your main KPIs in one place.
Organizing and Analyzing Data
Once you have collected relevant data, the next step is to organize and analyze it effectively. Without proper organization and analysis, all the collected data will be of no use in making informed decisions.
Personally, I like visual overviews to help me understand how something looked over time. For example, the Google Search Console is a good example.
Or the monthly ahrefs overview of keyword rankings:
It’s essential to choose the right analysis technique for your specific data set and decision-making needs. For example, if you are focusing on improving search rankings, look at keywords ranking and how high. Then work on getting them to move up in the rankings an getting more clicks from those people searching.
Identifying Patterns and Trends
Looking for trends is another important tactic to determine whether or not your content program is on the right track and where things can be improved.
There are several ways to spot trends:
- Look for a consistent direction in the data over time (e.g., a steady increase or decrease)
- Look for outliers or unusual data points that may indicate a shift in the data
- Use moving averages to smooth out fluctuations in the data and reveal underlying trends
It’s important to remember that patterns and trends may not always be obvious. It may take some digging and analysis to uncover them. However, once you do, they can provide valuable insights that can inform your decision-making process.
Overcoming Challenges in Using Data
One big challenge for anyone in marketing really is to remember to look at the right data. That means we have to actually review what’s happening and do so at the right interval. Email alerts can help with this. If the program you use offers the option to send emails when something noteworthy is happening.
Data Quality
A significant challenge in using data is ensuring its quality. For example, if your web metrics provider isn’t filtering out bot traffic correctly, that can be an issue.
Another issue can occur when companies don’t filter out internal employee traffic. For example, if employee visits to a website get measured, of course, it will look like there’s higher engagement. But it’s not real engagement and is a data quality issue. So filter out employee IP addresses from all metrics.
Data Interpretation
Interpreting data can be a complex process, and misinterpretation can lead to incorrect conclusions. To overcome this challenge, it’s important to have a clear understanding of the data you are working with and the context in which it was collected.
For example, when it comes to search rankings, I’ve seen creators celebrate a No. 1 ranking for a term that nobody actually searches for. Technically, the data isn’t incorrect, but the result and interpretation is quite useless – at least today.
Data Bias
Don’t spin the data to whatever story you want to tell. And when there’s not a clear story available around the data, don’t make it up. Test, test, test.
Conclusion
Overcoming challenges in using data is vital to ensuring that the information you rely on is accurate and actionable. By focusing on data quality, interpretation, and bias, you can use data effectively to inform your decision-making and drive success with your corporate storytelling long-term.